Developing a forest data portal to support multi-scale decision making

Forests play a pivotal role in timber production, maintenance and development of biodiversity and in carbon sequestration and storage in the context of the Kyoto Protocol. Policy makers and forest experts therefore require reliable information on forest extent, type and change for management, planning and modeling purposes. It is becoming increasingly clear that such forest information is frequently inconsistent and unharmonised between countries and continents. This research paper presents a forest information portal that has been developed in line with the GEOSS and INSPIRE frameworks. The web portal provides access to forest resources data at a variety of spatial scales, from global through to regional and local, as well as providing analytical capabilities for monitoring and validating forest change. The system also allows for the utilisation of forest data and processing services within other thematic areas. The web portal has been developed using open standards to facilitate accessibility, interoperability and data transfer.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Obstacles and solutions to maximising urban biodiversity throughout the lifecycles of major developments in England

Thus far, achieving net biodiversity gains through major urban developments has been neither common nor straightforward - despite the presence of incentives, regulatory contexts, and ubiquitous practical guidance tools. A diverse set of obstructions, occurring within different spatial, temporal and actor hierarchies, are experienced by practitioners and render the realisation of maximised biodiversity, a rarity. This research aims to illuminate why this is so, and what needs to be changed to rectify the situation. To determine meaningful findings and conclusions, capable of assisting applied contexts and accommodating a diverse range of influences, a ‘systems approach’ was adopted. This approach led to the use of a multi-strategy research methodology, to identify the key obstructions and solutions to protecting and enhancing biodiversity - incorporating the following methods: action research, a questionnaire to local government ecologists, interviews and personal communications with leading players, and literature reviews. Nevertheless, ‘case studies’ are the predominant research method, the focus being a ‘nested’ case study looking at strategic issues of the largest regeneration area in Europe ‘the Thames Gateway’, and the largest individual mixeduse mega-development in the UK (at the time of planning consent) ‘Eastern Quarry 2’ - set within the Gateway. A further key case study, focussing on the Central Riverside development in Sheffield, identifies the merits of competition and partnership. The nested cases, theories and findings show that the strategic scale - generally relating to governance and prioritisation - impacts heavily upon individual development sites. It also enables the identification of various processes, mechanisms and issues at play on the individual development sites, which primarily relate to project management, planning processes, skills and transdisciplinary working, innovative urban biodiversity design capabilities, incentives, organisational cultures, and socio-ecological resilience. From these findings a way forward is mapped, spanning aspects from strategic governance to detailed project management.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Efforts towards adaptability enhancement:how manufacturing and service industries can learn from each other

This paper discusses the recent emerging efforts for adaptability enhancement of Japanese industries to cope with a volatile demand environment. It is based on an analysis of data obtained from respondent companies. The analysis is focused on both manufacturing and manufacturing- related service industries such as construction/maintenance, software supply, manufacturing consultation and logistics industries to highlight their current situation, the sense of crisis in Japanese companies and possible future directions in relation to the two industry sectors. The principal conclusion is that for most companies consideration of a revision or modification to its cost structure is an essential requirement for survival in the global competitive environment.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Scenario-based preparedness plan for floods

The increasing trend of disaster victims globally is posing a complex challenge for disaster management authorities. Moreover, to accomplish successful transition between preparedness and response, it is important to consider the different features inherent to each type of disaster. Floods are portrayed as one of the most frequent and harmful disasters, hence introducing the necessity to develop a tool for disaster preparedness to perform efficient and effective flood management. The purpose of the article is to introduce a method to simultaneously define the proper location of shelters and distribution centers, along with the allocation of prepositioned goods and distribution decisions required to satisfy flood victims. The tool combines the use of a raster geographical information system (GIS) and an optimization model. The GIS determines the flood hazard of the city areas aiming to assess the flood situation and to discard floodable facilities. Then, the multi-commodity multimodal optimization model is solved to obtain the Pareto frontier of two criteria: distance and cost. The methodology was applied to a case study in the flood of Villahermosa, Mexico, in 2007, and the results were compared to an optimized scenario of the guidelines followed by Mexican authorities, concluding that the value of the performance measures was improved using the developed method. Furthermore, the results exhibited the possibility to provide adequate care for people affected with less facilities than the current approach and the advantages of considering more than one distribution center for relief prepositioning.

Is pregnancy a teachable moment for diet and physical activity behaviour change? An interpretative phenomenological analysis of the experiences of women during their first pregnancy

OBJECTIVES: Pregnancy may provide a 'teachable moment' for positive health behaviour change, as a time when women are both motivated towards health and in regular contact with health care professionals. This study aimed to investigate whether women's experiences of pregnancy indicate that they would be receptive to behaviour change during this period. DESIGN: Qualitative interview study. METHODS: Using interpretative phenomenological analysis, this study details how seven women made decisions about their physical activity and dietary behaviour during their first pregnancy. RESULTS: Two women had required fertility treatment to conceive. Their behaviour was driven by anxiety and a drive to minimize potential risks to the pregnancy. This included detailed information seeking and strict adherence to diet and physical activity recommendations. However, the majority of women described behaviour change as 'automatic', adopting a new lifestyle immediately upon discovering their pregnancy. Diet and physical activity were influenced by what these women perceived to be normal or acceptable during pregnancy (largely based on observations of others) and internal drivers, including bodily signals and a desire to retain some of their pre-pregnancy self-identity. More reasoned assessments regarding benefits for them and their baby were less prevalent and influential. CONCLUSIONS: Findings suggest that for women who conceived relatively easily, diet and physical activity behaviour during pregnancy is primarily based upon a combination of automatic judgements, physical sensations, and perceptions of what pregnant women are supposed to do. Health professionals and other credible sources appear to exert less influence. As such, pregnancy alone may not create a 'teachable moment'. Statement of contribution What is already known on this subject? Significant life events can be cues to action with relation to health behaviour change. However, much of the empirical research in this area has focused on negative health experiences such as receiving a false-positive screening result and hospitalization, and in relation to unequivocally negative behaviours such as smoking. It is often suggested that pregnancy, as a major life event, is a 'teachable moment' (TM) for lifestyle behaviour change due to an increase in motivation towards health and regular contact with health professionals. However, there is limited evidence for the utility of the TM model in predicting or promoting behaviour change. What does this study add? Two groups of women emerged from our study: the women who had experienced difficulties in conceiving and had received fertility treatment, and those who had conceived without intervention. The former group's experience of pregnancy was characterized by a sense of vulnerability and anxiety over sustaining the pregnancy which influenced every choice they made about their diet and physical activity. For the latter group, decisions about diet and physical activity were made immediately upon discovering their pregnancy, based upon a combination of automatic judgements, physical sensations, and perceptions of what is normal or 'good' for pregnancy. Among women with relatively trouble-free conception and pregnancy experiences, the necessary conditions may not be present to create a 'teachable moment'. This is due to a combination of a reliance on non-reflective decision-making, perception of low risk, and little change in affective response or self-concept.